{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Titanic Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Populating the interactive namespace from numpy and matplotlib\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn import tree\n",
    "import seaborn as sns\n",
    "%pylab inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>ParCh</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3</td>\n",
       "      <td>Braund, Mr. Owen Harris</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>A/5 21171</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17599</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C85</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3</td>\n",
       "      <td>Heikkinen, Miss. Laina</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113803</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>C123</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3</td>\n",
       "      <td>Allen, Mr. William Henry</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>373450</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Survived Pclass  \\\n",
       "0            1       0.0      3   \n",
       "1            2       1.0      1   \n",
       "2            3       1.0      3   \n",
       "3            4       1.0      1   \n",
       "4            5       0.0      3   \n",
       "\n",
       "                                                Name     Sex   Age  SibSp  \\\n",
       "0                            Braund, Mr. Owen Harris    male  22.0      1   \n",
       "1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
       "2                             Heikkinen, Miss. Laina  female  26.0      0   \n",
       "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n",
       "4                           Allen, Mr. William Henry    male  35.0      0   \n",
       "\n",
       "   ParCh            Ticket     Fare Cabin Embarked  \n",
       "0      0         A/5 21171   7.2500   NaN        S  \n",
       "1      0          PC 17599  71.2833   C85        C  \n",
       "2      0  STON/O2. 3101282   7.9250   NaN        S  \n",
       "3      0            113803  53.1000  C123        S  \n",
       "4      0            373450   8.0500   NaN        S  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic=pd.read_csv('C:\\\\Users\\\\Rohan raj\\\\Desktop\\\\First steps into case studies\\\\Dataset_2_Titanic.txt', \n",
    "                    sep=',', quotechar='\"', encoding='ISO 8859-15')\n",
    "titanic.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data Exploration and Preparation\n",
    "\n",
    "Learn about data\n",
    "1. Are there missing data?\n",
    "2. Is it categorical? if not, min , max, avg values? if yes, what are the categories?\n",
    "3. distribution of variables\n",
    "4. Duplicate entry"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### This will helps us to give tha idea about missing data "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>column_name</th>\n",
       "      <th>missing_count</th>\n",
       "      <th>filling_factor</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Cabin</td>\n",
       "      <td>731</td>\n",
       "      <td>23.052632</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Age</td>\n",
       "      <td>185</td>\n",
       "      <td>80.526316</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Embarked</td>\n",
       "      <td>4</td>\n",
       "      <td>99.578947</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Survived</td>\n",
       "      <td>3</td>\n",
       "      <td>99.684211</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Fare</td>\n",
       "      <td>2</td>\n",
       "      <td>99.789474</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Sex</td>\n",
       "      <td>1</td>\n",
       "      <td>99.894737</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>PassengerId</td>\n",
       "      <td>0</td>\n",
       "      <td>100.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Pclass</td>\n",
       "      <td>0</td>\n",
       "      <td>100.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Name</td>\n",
       "      <td>0</td>\n",
       "      <td>100.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>SibSp</td>\n",
       "      <td>0</td>\n",
       "      <td>100.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>ParCh</td>\n",
       "      <td>0</td>\n",
       "      <td>100.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Ticket</td>\n",
       "      <td>0</td>\n",
       "      <td>100.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    column_name  missing_count  filling_factor\n",
       "0         Cabin            731       23.052632\n",
       "1           Age            185       80.526316\n",
       "2      Embarked              4       99.578947\n",
       "3      Survived              3       99.684211\n",
       "4          Fare              2       99.789474\n",
       "5           Sex              1       99.894737\n",
       "6   PassengerId              0      100.000000\n",
       "7        Pclass              0      100.000000\n",
       "8          Name              0      100.000000\n",
       "9         SibSp              0      100.000000\n",
       "10        ParCh              0      100.000000\n",
       "11       Ticket              0      100.000000"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "missing_df = titanic.isnull().sum(axis=0).reset_index()\n",
    "missing_df.columns = ['column_name', 'missing_count']\n",
    "missing_df['filling_factor'] = (titanic.shape[0] \n",
    "                                - missing_df['missing_count']) / titanic.shape[0] * 100\n",
    "missing_df = missing_df.sort_values('filling_factor').reset_index(drop = True)\n",
    "missing_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Handling Missing data\n",
    "\n",
    "Strategies\n",
    "<li> Drop any observation with missing age\n",
    "<li> Set NaN as 0 - in the case  continous / categorical variables, create some new category for Nan?\n",
    "<li> use average value..\n",
    "<li> create Ml algo to predict missing values(ex-linear regression)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic['Age']=titanic['Age'].interpolate()\n",
    "titanic.Age.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### categorical variable"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "titanic =titanic.drop([918,920]) #Two entries having name entered in place of class.\n",
    "                    #Now we have two options either to drop or search for the dataset and fill the data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0    584\n",
       "1.0    361\n",
       "Name: Survived, dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic.Survived.value_counts() # 0- Didn't survive 1- Survived"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.382010582010582"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic['Survived'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "male      612\n",
       "female    336\n",
       "Name: Sex, dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic.Sex.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3    521\n",
       "1    232\n",
       "2    195\n",
       "Name: Pclass, dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic.Pclass.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#titanic = titanic.dropna(subset=['Pclass','Age'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data Representation and Transformation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>ParCh</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>179</th>\n",
       "      <td>180</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3</td>\n",
       "      <td>Leonard, Mr. Lionel</td>\n",
       "      <td>male</td>\n",
       "      <td>36.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>LINE</td>\n",
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       "      <th>263</th>\n",
       "      <td>264</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>Harrison, Mr. William</td>\n",
       "      <td>male</td>\n",
       "      <td>40.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>112059</td>\n",
       "      <td>0.0</td>\n",
       "      <td>B94</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>271</th>\n",
       "      <td>272</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3</td>\n",
       "      <td>Tornquist, Mr. William Henry</td>\n",
       "      <td>male</td>\n",
       "      <td>25.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>LINE</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>277</th>\n",
       "      <td>278</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2</td>\n",
       "      <td>Parkes, Mr. Francis \"Frank\"</td>\n",
       "      <td>male</td>\n",
       "      <td>26.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>239853</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>S</td>\n",
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       "    <tr>\n",
       "      <th>302</th>\n",
       "      <td>303</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3</td>\n",
       "      <td>Johnson, Mr. William Cahoone Jr</td>\n",
       "      <td>male</td>\n",
       "      <td>19.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>LINE</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>413</th>\n",
       "      <td>414</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2</td>\n",
       "      <td>Cunningham, Mr. Alfred Fleming</td>\n",
       "      <td>male</td>\n",
       "      <td>38.50</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>239853</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>466</th>\n",
       "      <td>467</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2</td>\n",
       "      <td>Campbell, Mr. William</td>\n",
       "      <td>male</td>\n",
       "      <td>47.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>239853</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>481</th>\n",
       "      <td>482</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2</td>\n",
       "      <td>Frost, Mr. Anthony Wood \"Archie\"</td>\n",
       "      <td>male</td>\n",
       "      <td>29.50</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>239854</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>597</th>\n",
       "      <td>598</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3</td>\n",
       "      <td>Johnson, Mr. Alfred</td>\n",
       "      <td>male</td>\n",
       "      <td>49.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>LINE</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>633</th>\n",
       "      <td>634</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>Parr, Mr. William Henry Marsh</td>\n",
       "      <td>male</td>\n",
       "      <td>20.50</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>112052</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>674</th>\n",
       "      <td>675</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2</td>\n",
       "      <td>Watson, Mr. Ennis Hastings</td>\n",
       "      <td>male</td>\n",
       "      <td>24.50</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>239856</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>732</th>\n",
       "      <td>733</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2</td>\n",
       "      <td>Knight, Mr. Robert J</td>\n",
       "      <td>male</td>\n",
       "      <td>17.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>239855</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>806</th>\n",
       "      <td>807</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>Andrews, Mr. Thomas Jr</td>\n",
       "      <td>male</td>\n",
       "      <td>39.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>112050</td>\n",
       "      <td>0.0</td>\n",
       "      <td>A36</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>815</th>\n",
       "      <td>816</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>Fry, Mr. Richard</td>\n",
       "      <td>male</td>\n",
       "      <td>26.75</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>112058</td>\n",
       "      <td>0.0</td>\n",
       "      <td>B102</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>822</th>\n",
       "      <td>823</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>Reuchlin, Jonkheer. John George</td>\n",
       "      <td>male</td>\n",
       "      <td>38.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>19972</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     PassengerId  Survived Pclass                              Name   Sex  \\\n",
       "179          180       0.0      3               Leonard, Mr. Lionel  male   \n",
       "263          264       0.0      1             Harrison, Mr. William  male   \n",
       "271          272       1.0      3      Tornquist, Mr. William Henry  male   \n",
       "277          278       0.0      2       Parkes, Mr. Francis \"Frank\"  male   \n",
       "302          303       0.0      3   Johnson, Mr. William Cahoone Jr  male   \n",
       "413          414       0.0      2    Cunningham, Mr. Alfred Fleming  male   \n",
       "466          467       0.0      2             Campbell, Mr. William  male   \n",
       "481          482       0.0      2  Frost, Mr. Anthony Wood \"Archie\"  male   \n",
       "597          598       0.0      3               Johnson, Mr. Alfred  male   \n",
       "633          634       0.0      1     Parr, Mr. William Henry Marsh  male   \n",
       "674          675       0.0      2        Watson, Mr. Ennis Hastings  male   \n",
       "732          733       0.0      2              Knight, Mr. Robert J  male   \n",
       "806          807       0.0      1            Andrews, Mr. Thomas Jr  male   \n",
       "815          816       0.0      1                  Fry, Mr. Richard  male   \n",
       "822          823       0.0      1   Reuchlin, Jonkheer. John George  male   \n",
       "\n",
       "       Age  SibSp  ParCh  Ticket  Fare Cabin Embarked  \n",
       "179  36.00      0      0    LINE   0.0   NaN        S  \n",
       "263  40.00      0      0  112059   0.0   B94        S  \n",
       "271  25.00      0      0    LINE   0.0   NaN        S  \n",
       "277  26.00      0      0  239853   0.0   NaN        S  \n",
       "302  19.00      0      0    LINE   0.0   NaN        S  \n",
       "413  38.50      0      0  239853   0.0   NaN        S  \n",
       "466  47.00      0      0  239853   0.0   NaN        S  \n",
       "481  29.50      0      0  239854   0.0   NaN        S  \n",
       "597  49.00      0      0    LINE   0.0   NaN        S  \n",
       "633  20.50      0      0  112052   0.0   NaN        S  \n",
       "674  24.50      0      0  239856   0.0   NaN        S  \n",
       "732  17.00      0      0  239855   0.0   NaN        S  \n",
       "806  39.00      0      0  112050   0.0   A36        S  \n",
       "815  26.75      0      0  112058   0.0  B102        S  \n",
       "822  38.00      0      0   19972   0.0   NaN        S  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic[titanic.Fare == 0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Droping some of the columns which many not contribute much to our machine learning model such as Name, Ticket, Cabin etc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "titanic['Pclass']=titanic['Pclass'].astype(str).astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 948 entries, 0 to 949\n",
      "Data columns (total 12 columns):\n",
      "PassengerId    948 non-null int64\n",
      "Survived       945 non-null float64\n",
      "Pclass         948 non-null int32\n",
      "Name           948 non-null object\n",
      "Sex            948 non-null object\n",
      "Age            948 non-null float64\n",
      "SibSp          948 non-null int64\n",
      "ParCh          948 non-null int64\n",
      "Ticket         948 non-null object\n",
      "Fare           948 non-null float64\n",
      "Cabin          217 non-null object\n",
      "Embarked       946 non-null object\n",
      "dtypes: float64(3), int32(1), int64(3), object(5)\n",
      "memory usage: 92.6+ KB\n"
     ]
    }
   ],
   "source": [
    "titanic.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>ParCh</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.0</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.0</td>\n",
       "      <td>3</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.0</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Survived  Pclass     Sex   Age  SibSp  ParCh     Fare Embarked\n",
       "0       0.0       3    male  22.0      1      0   7.2500        S\n",
       "1       1.0       1  female  38.0      1      0  71.2833        C\n",
       "2       1.0       3  female  26.0      0      0   7.9250        S\n",
       "3       1.0       1  female  35.0      1      0  53.1000        S\n",
       "4       0.0       3    male  35.0      0      0   8.0500        S"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cols = ['Name','Ticket','Cabin','PassengerId']\n",
    "titanic_DT= titanic\n",
    "titanic_DT = titanic_DT.drop(cols,axis=1)\n",
    "titanic_DT.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Now we convert the Pclass, Sex, Embarked to columns in pandas and drop them after conversion."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "dummies = []\n",
    "cols = ['Pclass','Sex','Embarked']\n",
    "for col in cols:\n",
    " dummies.append(pd.get_dummies(titanic_DT[col]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>female</th>\n",
       "      <th>male</th>\n",
       "      <th>C</th>\n",
       "      <th>Q</th>\n",
       "      <th>S</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   1  2  3  female  male  C  Q  S\n",
       "0  0  0  1       0     1  0  0  1\n",
       "1  1  0  0       1     0  1  0  0\n",
       "2  0  0  1       1     0  0  0  1\n",
       "3  1  0  0       1     0  0  0  1\n",
       "4  0  0  1       0     1  0  0  1"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic_dummies = pd.concat(dummies, axis=1)\n",
    "titanic_dummies.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Then  we concatenate to the original dataframe columnwise"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "titanic_DT = pd.concat((titanic_DT,titanic_dummies),axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>ParCh</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Embarked</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
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       "      <th>female</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
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       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
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       "      <td>7.2500</td>\n",
       "      <td>S</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.0</td>\n",
       "      <td>3</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>S</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>S</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.0</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>S</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Survived  Pclass     Sex   Age  SibSp  ParCh     Fare Embarked  1  2  3  \\\n",
       "0       0.0       3    male  22.0      1      0   7.2500        S  0  0  1   \n",
       "1       1.0       1  female  38.0      1      0  71.2833        C  1  0  0   \n",
       "2       1.0       3  female  26.0      0      0   7.9250        S  0  0  1   \n",
       "3       1.0       1  female  35.0      1      0  53.1000        S  1  0  0   \n",
       "4       0.0       3    male  35.0      0      0   8.0500        S  0  0  1   \n",
       "\n",
       "   female  male  C  Q  S  \n",
       "0       0     1  0  0  1  \n",
       "1       1     0  1  0  0  \n",
       "2       1     0  0  0  1  \n",
       "3       1     0  0  0  1  \n",
       "4       0     1  0  0  1  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic_DT.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Now that we converted Pclass, Sex, Embarked values into columns, we drop the redundant same columns from the dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Survived</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>ParCh</th>\n",
       "      <th>Fare</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>female</th>\n",
       "      <th>male</th>\n",
       "      <th>C</th>\n",
       "      <th>Q</th>\n",
       "      <th>S</th>\n",
       "    </tr>\n",
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       "  <tbody>\n",
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       "      <th>0</th>\n",
       "      <td>0.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "      <td>1</td>\n",
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       "      <td>1.0</td>\n",
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       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Survived   Age  SibSp  ParCh     Fare  1  2  3  female  male  C  Q  S\n",
       "0       0.0  22.0      1      0   7.2500  0  0  1       0     1  0  0  1\n",
       "1       1.0  38.0      1      0  71.2833  1  0  0       1     0  1  0  0\n",
       "2       1.0  26.0      0      0   7.9250  0  0  1       1     0  0  0  1\n",
       "3       1.0  35.0      1      0  53.1000  1  0  0       1     0  0  0  1\n",
       "4       0.0  35.0      0      0   8.0500  0  0  1       0     1  0  0  1"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic_DT = titanic_DT.drop(['Pclass','Sex','Embarked'],axis=1)\n",
    "titanic_DT.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 948 entries, 0 to 949\n",
      "Data columns (total 13 columns):\n",
      "Survived    945 non-null float64\n",
      "Age         948 non-null float64\n",
      "SibSp       948 non-null int64\n",
      "ParCh       948 non-null int64\n",
      "Fare        948 non-null float64\n",
      "1           948 non-null uint8\n",
      "2           948 non-null uint8\n",
      "3           948 non-null uint8\n",
      "female      948 non-null uint8\n",
      "male        948 non-null uint8\n",
      "C           948 non-null uint8\n",
      "Q           948 non-null uint8\n",
      "S           948 non-null uint8\n",
      "dtypes: float64(3), int64(2), uint8(8)\n",
      "memory usage: 51.8 KB\n"
     ]
    }
   ],
   "source": [
    "titanic_DT.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### All is good, except Survived which has few missing values. Pandas has a nice interpolate() function that will replace all the missing NaNs to interpolated values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "titanic_DT['Survived'] = titanic_DT['Survived'].interpolate()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 948 entries, 0 to 949\n",
      "Data columns (total 13 columns):\n",
      "Survived    948 non-null float64\n",
      "Age         948 non-null float64\n",
      "SibSp       948 non-null int64\n",
      "ParCh       948 non-null int64\n",
      "Fare        948 non-null float64\n",
      "1           948 non-null uint8\n",
      "2           948 non-null uint8\n",
      "3           948 non-null uint8\n",
      "female      948 non-null uint8\n",
      "male        948 non-null uint8\n",
      "C           948 non-null uint8\n",
      "Q           948 non-null uint8\n",
      "S           948 non-null uint8\n",
      "dtypes: float64(3), int64(2), uint8(8)\n",
      "memory usage: 51.8 KB\n"
     ]
    }
   ],
   "source": [
    "titanic_DT.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Now we convert our dataframe from pandas to numpy and we assign input and output"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "y = titanic_DT['Survived'].values #output"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th>2</th>\n",
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       "      <td>1</td>\n",
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       "      <th>3</th>\n",
       "      <td>1.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>1</td>\n",
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       "      <td>1</td>\n",
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       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Survived   Age  SibSp  ParCh     Fare  1  2  3  female  male  C  Q  S\n",
       "0       0.0  22.0      1      0   7.2500  0  0  1       0     1  0  0  1\n",
       "1       1.0  38.0      1      0  71.2833  1  0  0       1     0  1  0  0\n",
       "2       1.0  26.0      0      0   7.9250  0  0  1       1     0  0  0  1\n",
       "3       1.0  35.0      1      0  53.1000  1  0  0       1     0  0  0  1\n",
       "4       0.0  35.0      0      0   8.0500  0  0  1       0     1  0  0  1"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic_DT.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### X has still Survived values in it, which should not be there. So we drop in numpy column which is the 1st column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>ParCh</th>\n",
       "      <th>Fare</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>female</th>\n",
       "      <th>male</th>\n",
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       "      <th>2</th>\n",
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       "      <td>1</td>\n",
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       "      <th>3</th>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Age  SibSp  ParCh     Fare  1  2  3  female  male  C  Q  S\n",
       "0  22.0      1      0   7.2500  0  0  1       0     1  0  0  1\n",
       "1  38.0      1      0  71.2833  1  0  0       1     0  1  0  0\n",
       "2  26.0      0      0   7.9250  0  0  1       1     0  0  0  1\n",
       "3  35.0      1      0  53.1000  1  0  0       1     0  0  0  1\n",
       "4  35.0      0      0   8.0500  0  0  1       0     1  0  0  1"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic_ml= titanic_DT.drop(['Survived'],1)\n",
    "X = titanic_ml.values\n",
    "titanic_ml.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Now that we are ready with X and y, lets split the dataset for 70% Training and  30% test set using train_test split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X,y,test_size=0.30,random_state=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Lets start with simple Decision Tree Classifier machine learning algorithm and see how it goes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8105263157894737"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf = tree.DecisionTreeClassifier(max_depth=4)\n",
    "clf.fit(X_train,y_train)\n",
    "clf.score(X_test,y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Decision trees compute entropy in the information system. If you peform a decision tree on dataset, the variable importances_ contains important information on what columns of data has large variances thus contributing to the decision. Lets see the output"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### This output shows that second element in array 0.09,  “Age” has 9% importance, 'SibSp' has 6.8%, 'fare' has 5.4%, the last 4th shows 56.3% which is for  ‘Females’."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(0.09019173856770002, 0.06835898161995453, 0.02689369562999676, 0.05420941371155693, 0.053475663663459676, 0.006341278443409511, 0.12030151164726244, 0.0, 0.5630381290826999, 0.0, 0.0, 0.017189587633960255) ('Age', 'SibSp', 'ParCh', 'Fare', 1, 2, 3, 'female', 'male', 'C', 'Q', 'S')\n"
     ]
    }
   ],
   "source": [
    "imp=clf.feature_importances_\n",
    "names=titanic_ml.columns\n",
    "imp,names=zip(*(zip(imp,names)))\n",
    "print(imp,names)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data Visualization and Presentation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1b421bf4cf8>"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(x=\"Pclass\", y=\"Age\", data=titanic)\n",
    "sns.swarmplot(x=\"Pclass\", y=\"Age\", data=titanic, color=\".25\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1b421cf12e8>"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "titanic.plot.scatter(x=\"Fare\", y=\"Pclass\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1b421d9cf28>"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Womens and mens surviving ratio\n",
    "\n",
    "fig, axs =plt.subplots(1,2)\n",
    "titanic[titanic.Sex =='male'].Survived.value_counts().plot(kind='barh',ax=axs[0], title ='male Survivors')\n",
    "titanic[titanic.Sex =='female'].Survived.value_counts().plot(kind='barh',ax=axs[1], title ='Female Survivors')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1b421e42ac8>"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Male and female survival by their age\n",
    "fig, axs =plt.subplots(1,2)\n",
    "titanic[(titanic.Sex =='male') & (titanic.Age < 15)].Survived.value_counts().plot(kind='bar',ax=axs[0], \n",
    "                                                                                  title ='Male child Survivors')\n",
    "titanic[(titanic.Sex =='female') & (titanic.Age < 15)].Survived.value_counts().plot(kind='bar',ax=axs[1], \n",
    "                                                                                    title ='Female  child Survivors')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Survived</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>0.940000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>female</td>\n",
       "      <td>0.898734</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3</td>\n",
       "      <td>female</td>\n",
       "      <td>0.503226</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>male</td>\n",
       "      <td>0.369231</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2</td>\n",
       "      <td>male</td>\n",
       "      <td>0.155172</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>0.142466</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Pclass     Sex  Survived\n",
       "0       1  female  0.940000\n",
       "2       2  female  0.898734\n",
       "4       3  female  0.503226\n",
       "1       1    male  0.369231\n",
       "3       2    male  0.155172\n",
       "5       3    male  0.142466"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic[['Pclass', 'Survived','Sex']].groupby(['Pclass','Sex'], as_index=False).mean().sort_values(by='Survived', ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.382010582010582"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic['Survived'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Sex</th>\n",
       "      <th>Survived</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>female</td>\n",
       "      <td>0.727545</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>male</td>\n",
       "      <td>0.193126</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Sex  Survived\n",
       "0  female  0.727545\n",
       "1    male  0.193126"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic[['Sex', 'Survived']].groupby(['Sex'], as_index=False).mean().sort_values(by='Survived', ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.5384615384615384 0.6451612903225806\n"
     ]
    }
   ],
   "source": [
    "print(titanic[(titanic.Sex =='male') & (titanic.Age < 10)].Survived.mean(),\n",
    "titanic[(titanic.Sex =='female') & (titanic.Age < 10)].Survived.mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.6923076923076923 1.0\n"
     ]
    }
   ],
   "source": [
    "print(titanic[(titanic.Sex =='male') & (titanic.Age < 2)].Survived.mean(),\n",
    "titanic[(titanic.Sex =='female') & (titanic.Age < 2)].Survived.mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x000001B421EAF668>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x000001B421E85EB8>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x000001B421F0CA58>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x000001B421F36FD0>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x000001B421F64588>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x000001B421F8DB00>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x000001B421FBE0B8>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x000001B421FE4668>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x000001B421FE46A0>]],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 9 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "cols=['Pclass','Age','Survived']\n",
    "pd.plotting.scatter_matrix(titanic[cols], figsize = (10, 10), diagonal = 'kde')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   FamilySize  Survived\n",
      "0           1  0.301954\n",
      "1           2  0.544944\n",
      "2           3  0.579439\n",
      "3           4  0.687500\n",
      "4           5  0.187500\n",
      "5           6  0.125000\n",
      "6           7  0.333333\n",
      "7           8  0.000000\n",
      "8          11  0.000000\n"
     ]
    }
   ],
   "source": [
    "\n",
    "titanic_age= titanic\n",
    "for dataset in titanic:\n",
    "    titanic_age['FamilySize'] = titanic['SibSp'] + titanic['ParCh'] + 1\n",
    "print (titanic_age[['FamilySize', 'Survived']].groupby(['FamilySize'], as_index=False).mean())"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python [default]",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
